Using machine learning to speed up new and upgrade detector studies: a calorimeter case
This work addresses the problem of slow and scattered detector R&D for physicists, particularly in high-energy physics, by providing an incremental automation approach using existing methods on new data.
The paper tackles the challenge of speeding up detector design and upgrade studies by applying machine learning to automate and optimize the R&D process, specifically demonstrating improved spatial reconstruction and time of arrival properties for an electromagnetic calorimeter upgrade in the LHCb detector.
In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design concept (CDR) and technical design (TDR) phases of future detectors and existing detectors if upgraded. The machine learning approaches may speed up the verification of the possible detector configurations and will automate the entire detector R\&D, which is often accompanied by a large number of scattered studies. We present the approach of using machine learning for detector R\&D and its optimisation cycle with an emphasis on the project of the electromagnetic calorimeter upgrade for the LHCb detector\cite{lhcls3}. The spatial reconstruction and time of arrival properties for the electromagnetic calorimeter were demonstrated.